Provides control and management of Jira through Jira Command Line, enabling interaction with Jira issues, projects, and workflows.
Jira MCP
Jira MCP for controlling Jira through Jira Command Line.
Installation
Option 1: Development setup with uv
Get repo:
Add MCP server to your choice of LLM client:
NOTE: You will need to look up for your specific client on how to add MCPs.
Usually the JSON file for the LLM client will look like this:
This will tell your LLM client application that there's a tool that can be
called by calling uv --directory /ABSOLUTE/PATH/TO/REPO run python -m src.main.
Install UV: https://docs.astral.sh/uv/getting-started/installation/
Option 2: Install globally with pipx
How it works
You enter some questions or prompt to a LLM Client such as the Claude Desktop, Cursor, Windsurf, or ChatGPT.
The client sends your question to the LLM model (Sonnet, Grok, ChatGPT)
LLM analyzes the available tools and decides which one(s) to use
The LLM you're using will have a context of the tools and what each tool is meant for in human language.
Alternatively without MCPs, you could include in the prompt the endpoints and a description on each endpoint for the LLM to "call on". Then you could copy and paste the text commands into the terminal on your machine.
MCPs provide a more deterministic and standardized method on LLM-to-server interactions.
The client executes the chosen tool(s) through the MCP server.
The MCP server is either running local on your machine or an endpoint hosting the MCP server remotely.
The results are sent back to LLM.
LLM formulates a natural language response and one or both of the following happen:
The response is displayed to you with data from the MCP server
Some action is performed using the MCP server
Development
Logging
Do not use print statements for logging. Use the logging module instead.
Writing to stdout will corrupt the JSON-RPC messages and break your server.
Docstrings / Tool decorator parameters
MCP.tools decorator parameters are especially important as this is the human readable text that the LLM has context of. This will be treated as part of the prompt when fed to the LLM and this will decide when to use each tool.
Architecture
MCP follows a client-server architecture where an MCP host (an AI application like Cursor or ChatGPT desktop) establishes connections to one or more MCP servers. The MCP host accomplishes this by creating one MCP client for each MCP server. Each MCP client maintains a dedicated connection with its corresponding MCP server.